The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Water, often received via rain or other precipitation, is an essential element to life. For farmers, rainfall is a large factor in determining how much water a crop receives, thereby altering the potential yield for the crop. While rainfall has many positive effects, such as giving life to crops, large quantities of rainfall can also have severe repercussions, such as by causing floods or resulting in standing or ponding water that can inundate seedlings or mature crops. Thus, accurate measurements of rainfall can be extremely important, both to maximize gains from the rainfall and minimize risks from an overabundance of rainfall.
A common approach for measuring rainfall involves utilizing radar data to calculate the rainfall. Generally, a polarized beam of energy is emitted from a radar device in a particular direction. The beam travels un-disturbed before encountering a volume of air containing hydrometeors, such as rainfall, snowfall, or hail, which causes the beam to scatter energy back to a radar receiver. Based on the amount of time it takes for a radar beam to return, the distance between the radar device and the volume of air containing hydrometeors is computed. The amount of energy that is received by the radar, also known as the reflectivity, is used to compute the rainfall rate. Often, the relationship between the reflectivity and the actual rainfall rate is modeled through the Z-R transformation:Z=aRb where Z is the reflectivity and R is the actual rainfall rate. The parameters for the Z-R transformation may be identified through measurements for rain gauges for a particular area and type of storm.
A drawback with using radar reflectivity to measure the rainfall rate is that radar systems are unable to take continuous measurements of reflectivity values. Instead, radar systems produce reflectivity data at discrete instances which are separated by an interval of time that is dependent on the speed at which the radar device can take successive measurements. Precipitation rate estimates are thus constrained by the speed of the radar device. For example, the fastest a radar can fully and reliably sample the surrounding environment is approximately four and a half minutes. While the radar may be able to send signals faster, the radar would not be able to distinguish signals received from a first sampling from radar signals received from a second sampling at a lower interval. For slow moving storms with low changes in intensity of precipitation, radar based precipitation estimates are adequate to produce hourly or daily accumulation at all locations.
The constraint of the radar devices creates difficulty in estimating precipitation for storms that rapidly increase or decrease in intensity. Additionally, the constraint of the radar devices creates difficulty in estimating precipitation for fast moving storms. For example, small convective storms can move extremely quickly over a region. Fast moving convective storms, such as supercells, can travel at up to seventy miles an hour. Between two successive measurements by the radar device, a storm may pass undetected over a region of land. While the radar device would process reflectivity data at the starting location and ending location of the storm, no radar reflectivity data would be available for the areas that the storm passed over in between measurements. Thus, even though it rained in a particular location in between the measurements, no precipitation rate estimate would be available for the particular location.
Often, it is important for a farmer to understand whether or not it rained at a particular field. With hundreds of acres of farmland to cover, a farmer may not be able to observer each portion of the field to determine where the farmland is wet and where the farmland is dry. Additionally, when too much water accumulates on a field, the field becomes unworkable. Thus, it becomes important to track supercells of a storm which may comprise higher rates of precipitation. If precipitation rate estimates are incomplete because they do not include estimates in locations where it rained or the estimates are low due to the movement of supercells of a storm, a farmer relying on the information may make decisions that adversely affect the crops on the field, such as determining whether to work on the field on a given day.
Thus, there is a need for a system which generates precipitation estimates for locations and times where radar based precipitation estimates are unavailable.